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| Convex Discriminant Canonical Correlation Analysis |
| JIANG Fan, CHEN Songcan |
| College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106 |
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Abstract Inspired by geometric mean metric learning(GMML), a convex discriminant canonical correlation analysis(CDCA) is proposed. The learning of two projection matrices is transformed into a geodesic convex problem of metric learning. Thereby a closed form solution is acquired and simultaneously discriminant fused features are extracted directly. The experiments on artificial and real datasets verify the effectiveness of CDCA.
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Received: 04 May 2017
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About author:: (JIANG Fan, born in 1985, master student. His research interests include pattern recognition and machine learning.)
(CHEN Songcan(Corresponding author), born in 1962, Ph.D., professor. His research interests include pattern recognition and machine learning.) |
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2017, Vol. 30 
